Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models


Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Publication DOI: https://doi.org/10.1186/1471-2105-7-182
Divisions: College of Health & Life Sciences > Aston Pharmacy School
College of Health & Life Sciences
Additional Information: © 2006 Liu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publication ISSN: 1471-2407
Last Modified: 08 Dec 2023 09:11
Date Deposited: 02 Feb 2015 15:55
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Related URLs: http://www.biom ... 1471-2105/7/182 (Publisher URL)
PURE Output Type: Article
Published Date: 2006-03-31
Authors: Liu, Wen
Meng, Xiangshan
Xu, Qiqi
Flower, Darren R. (ORCID Profile 0000-0002-8542-7067)
Li, Tongbin



Version: Published Version

License: Creative Commons Attribution

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